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Tail-Sensitive Convergence Guarantees for Unadjusted Hamiltonian Monte Carlo
I’m pleased to share a new paper with Siddharth Mitra and Andre Wibisono (both at Yale), titled: Tail-Sensitive KL and Rényi Convergence of Unadjusted Hamiltonian Monte Carlo via One-Shot Couplings. … Read More
Extending No-U-Turn Ideas to Discrete State Spaces
How far can the ideas behind the No-U-Turn Sampler really go? We’ve been thinking about this question for some time, and it motivates recent work with my student Zichu Wang … Read More
Decoupling for Markov Chains
How can we rigorously quantify Monte Carlo error and assess convergence in modern MCMC methods such as the No-U-Turn Sampler? This question motivates new joint work with Victor de la … Read More